Modernizing the undergraduate regression analysis course
eCOTS 2022
Monday, May 23, 2022
3:35 pm – 4:45 pm ET
Abstract
There has been significant innovation in introductory statistics and data science courses to equip students with the statistical, computing, and communication skills needed for modern data analysis. Innovating subsequent courses is also important, so students can continue developing these skills beyond the first course. In this session, we’ll present a modern approach to teaching undergraduate regression analysis, the second statistics course for many students. We’ll share strategies for using real-world data sets and examples, teaching modern computing skills, and incorporating non-technical skills such as writing and effective collaboration as part of the course. We’ll share example activities and assignments, along with a demo of the computing toolkit using the R tidymodels package, Quarto for reproducible reports, and Git and GitHub for version control and collaboration. The activities and demo will be hands-on; attendees will also have the opportunity to exchange ideas and ask questions throughout the session.
Presenters
Dr. Maria Tackett is an Assistant Professor of the Practice in the Department of Statistical Science at Duke University. Her current work focuses on understanding how active learning strategies can be used to promote engagement and student motivation in undergraduate statistics courses. She also studies how classroom practices in introductory math and statistics courses impact students’ sense of community, self-efficacy, and learning outcomes. Maria is an RStudio certified trainer and is actively involved in the R and statistics education communities.
Dr. Mine Çetinkaya-Rundel (she/her) is Professor of the Practice and Director of Undergraduate Studies at the Department of Statistical Science at Duke University. Mine’s work focuses on innovation in statistics and data science pedagogy, with an emphasis on computing, reproducible research, student-centered learning, and open-source education as well as pedagogical approaches for enhancing retention of women and under-represented minorities in STEM. Mine also works with RStudio as a Developer Educator.
Risk Presman is a rising 3rd year PhD student in the Department of Statistical Science at Duke University. He works on problems that lie at the intersection of statistical inference and optimization. Most recently, Rick completed an MS in Statistics at the University of Chicago and worked as a consultant in financial services. Prior to that, he completed a BS in Mathematics (Honors) and a BA in Economics at the University of Chicago.